In the past few years, grid computing has shown a tremendous growth in the computer industry. Grid computing is a form of a distributed computing system in which numerous users share computing resources. The grid computing expedites selection, aggregation, and sharing of the computing resources across multiple hops (or nodes) over a diverse geographic area and across multiple networks. The sharing may be based on availability, capability, and cost of the computing resources available in the grid as well as on a user's quality of service requirements and the user's functional demands. Grid computing is growing in popularity to address the growing demands of processing power and capability.
In order to meet the growing demand of processing and a high performance in the distributed computing system, it is increasingly becoming critical to minimize latency between the multiple nodes. Latency in the network increases the possibilities of having bottlenecks at different hops in the grid due to factors, external to the system like network timeouts, CPU/memory bottlenecks and so forth. Latency also increases the packet loss in the network. Therefore, minimizing latency in the distributed system can result in aggregated and improved efficiency of computing, data, and storage resources.
In common approach, systems try to minimize latency in a packet-to-packet transmission. Such approach is expensive in terms of both space and time. In a conventional approach, systems attempt to minimize latency by synchronizing data packets only on source and destination clocks. Accordingly, these systems do not provide the functionality necessary to optimize and minimize system latency between remotely operating computing devices. In another conventional approach, a data stream is modified to include a time stamp synchronized to a common clock. This method is also intrusive and can delay distribution of the data stream.
In light of the above discussion, there is a need for a method and system, which overcomes all the above stated problems.